Abstract 1866: Transcriptomics analysis for identification of pathways involved in the response to Tumor Treating Fields (TTFields)
Authors: Kerem Wainer-Katsir, Gitit Lavy-Shahaf, Shiri Davidi, Sara Jacobovitch, Tali Voloshin, Itai Tzchori, Yaara Porat, Lianghao Ding, Michael Story, Niv Pencovich, Ilan Volovitz, Joshua Branter, Stuart J. Smith, Adi Haber, Moshe Giladi, Uri Weinberg, Yoram Palti
Journal: Cancer Research (2022)

Abstract Introduction: Tumor Treating Fields (TTFields) are alternating electric fields that disrupt the function of polarized molecules within cancer cells. Initial data showed an anti-mitotic effect on cancerous cells whilst more recent findings confirmed additional effects such as downregulation of DNA double strand break repair, replication stress, upregulation of autophagy, and immunogenic cell death. Identification of TTFields-driven alterations in pan-cancer and tumor specific pathway is needed to aid selection of therapeutic modalities to be applied concomitant with TTFields for improved treatment outcomes. The aim of this study was to identify common pathways involved in the response to TTFields by comparing transcriptomics of various tumor type. Methods: Control and TTFields-treated non-small cell lung carcinoma cell lines and animal model, glioblastoma (GBM) cell lines and patient-derived cell lines, and a hepatocellular carcinoma animal model were examined. Samples from GBM patients treated with concomitant TTFields and temozolomide (TMZ) were compared to samples from patients treated with TMZ alone. A list of differentially expressed genes (DEGs) was generated from transcriptomics analysis. Enrichment analysis was conducted according to the Gene Set Enrichment Analysis (GSEA) of MSigDB, Reactome, and Kegg pathway databases. Significantly overlapping pathways were identified using ActivePathways package according to the Reactome and Kegg gene sets, and an enrichment map was created according to the number of datasets supporting each pathway. Results: DEGs in response to TTFields application included genes of the cytoskeleton, immune system, and some secretion proteins. Common pathways downregulated by TTFields (negative GSEA) included DNA repair, DNA and RNA synthesis, and cell cycle regulation, specifically the G2M checkpoint, E2F targets, and Myc targets. Common pathways upregulated by TTFields (positive GSEA) included the immune response, specifically complement cascades, coagulation, and lysosome activity. ActivePathways enrichment map results agreed with the gene set enrichment results, and revealed additional pathways involved in the response to TTFields, such as SUMOylation, metabolism of carbohydrates, unfolded protein response, and signaling by interleukins. Conclusions: Transcriptomic analysis revealed common pathways involved in the responses to TTFields, regardless of the origin of the sample. Some identified pathways were in line with previously demonstrated effect of TTFields, such as mitotic interference, inhibition of DNA damage repair and upregulation of the innate immune response. New pathways revealed in this work support the examination of novel combination strategies with TTFields to increase the therapeutic effect in patients bearing various solid tumor types. Citation Format: Kerem Wainer-Katsir, Gitit Lavy-Shahaf, Shiri Davidi, Sara Jacobovitch, Tali Voloshin, Itai Tzchori, Yaara Porat, Lianghao Ding, Michael Story, Niv Pencovich, Ilan Volovitz, Joshua Branter, Stuart J. Smith, Adi Haber, Moshe Giladi, Uri Weinberg, Yoram Palti. Transcriptomics analysis for identification of pathways involved in the response to Tumor Treating Fields (TTFields) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1866.